Building English ASR model with regional language support

• URL: http://arxiv.org/abs/2503.07522v1
• Date: Mon, 10 Mar 2025 16:48:51 GMT
• Title: Building English ASR model with regional language support
• Authors: Purvi Agrawal, Vikas Joshi, Bharati Patidar, Ankur Gupta, Rupesh Kumar Mehta,
• Abstract summary: We present a novel approach to developing an English Automatic Speech Recognition (ASR) system that can effectively handle Hindi queries.<n>We propose a novel acoustic model (AM) that features shared hidden layers across languages and language-specific projection layers combined via a self-attention mechanism.<n>Our results demonstrate the effectiveness of our approach, with a 69.3% and 5.7% relative reduction in word error rate on Hindi and English test sets respectively.
• Score: 5.329085929355182
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In this paper, we present a novel approach to developing an English Automatic Speech Recognition (ASR) system that can effectively handle Hindi queries, without compromising its performance on English. We propose a novel acoustic model (AM), referred to as SplitHead with Attention (SHA) model, features shared hidden layers across languages and language-specific projection layers combined via a self-attention mechanism. This mechanism estimates the weight for each language based on input data and weighs the corresponding language-specific projection layers accordingly. Additionally, we propose a language modeling approach that interpolates n-gram models from both English and transliterated Hindi text corpora. Our results demonstrate the effectiveness of our approach, with a 69.3% and 5.7% relative reduction in word error rate on Hindi and English test sets respectively when compared to a monolingual English model.

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